Abstract

This paper explores the labor market and schooling effects of the Deferred Action for Childhood Arrivals (DACA) initiative, which provides work authorization to eligible immigrants along with a temporary reprieve from deportation. The analysis relies on a difference-in-differences approach which exploits the discontinuity in program rules to compare eligible individuals to ineligible, likely undocumented immigrants before and after the program went into effect. To address potential endogeneity concerns, we focus on youths that likely met DACA’s schooling requirement when the program was announced. We find that DACA reduced the probability of school enrollment of eligible higher-educated individuals, as well as some evidence that it increased the employment likelihood of men, in particular. Together, these findings suggest that a lack of authorization may lead individuals to enroll in school when working is not a viable option. Thus, once employment restrictions are relaxed and the opportunity costs of higher education rise, eligible individuals may reduce investments in schooling.

Keywords

JEL classification

Notes

Acknowledgments

We thank Kelly Bedard, Sarah Bohn, Brian Cadena, Seema Jayachandran, Terra McKinnish, Anita Alves Pena, Audrey Singer, Stephen J. Trejo, Klaus Zimmermann, three anonymous referees, seminar participants at the IZA Annual Migration Meeting, University of Southern California and Colegio de la Frontera, along with session participants at the annual meetings of the American Economic Association, Population Association of America and Western Economic Association International. Any errors are our own.

Compliance with ethical standards

Conflict of interest

The authors declare that they have no conflict of interest.

Appendix 1

Table 16

High- and low-skill occupations

High-skill occupations

1. “Management occupations”

2. “Business and financial operations occupations”

3. “Computer and mathematical science occupations”

4. “Architecture and engineering occupations”

5. “Life, physical, and social science occupations”

6. “Community and social service occupations”

7. “Legal occupations”

8. “Education, training, and library occupations”

9. “Arts, design, entertainment, sports, and media”

10. “Healthcare practitioner and technical occupations”

Low-skill occupations

11. “Healthcare support occupations”

12. “Protective service occupations”

13. “Food preparation and serving related occupations”

14. “Building and grounds cleaning and maintenance”

15. “Personal care and service occupations”

16. “Sales and related occupations”

17. “Office and administrative support occupations”

18. “Farming, fishing, and forestry occupations”

19. “Construction and extraction occupations”

20. “Installation, maintenance, and repair occupations”

21. “Production occupations”

22. “Transportation and material moving occupations”

23. “Armed Forces”

Appendix 2

Table 17

Results for non-citizens 18–24 years of age of all educational attainments (sample: non-citizen men and women)

Key regressors

Likelihood of being enrolled in school

Likelihood of being enrolled in school full-time

Likelihood of being employed

Usual weekly hours of work

Log real hourly wages

Likelihood of working in a high-skill occupation

DACA × eligible

−0.117***

(0.029)

−0.115***

(0.026)

0.068***

(0.025)

0.218

(0.693)

−0.014

(0.036)

−0.013

(0.030)

Eligible

0.212***

(0.010)

0.189***

(0.010)

−0.059***

(0.011)

−1.941***

(0.348)

−0.016

(0.014)

−0.009

(0.012)

Observations

19,177

19,177

19,177

8063

8063

8063

R-squared

0.339

0.313

0.153

0.162

0.125

0.202

Other covariates include: gender, race (white and black), marital status, indicators for age, years in the USA, number of children, educational attainment (HS and more than HS), an indicator for whether the individual resides in a state with any of the following immigration enforcement measures: E-Verify mandate, omnibus immigration law, or a 287(g) agreement, a separate indicator for residing in a state granting in-state tuition for undocumented immigrants, and state-level unemployment rates. Additionally, all specifications include state fixed effects, month-year fixed effects, and state-specific linear time trends. Standard errors are clustered at the state level

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